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Record W4388784514 · doi:10.1016/j.tncr.2023.08.004

Standards-based hierarchical governance of a digital trade network

2023· article· en· W4388784514 on OpenAlexvenueno aff
Lijuan Yang

Bibliographic record

VenueTransnational Corporation Review · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
FundersScience and Technology Program of Gansu ProvinceNational Social Science Fund of ChinaNational Office for Philosophy and Social Sciences
KeywordsTechnical standardHarmonizationCornerstoneCorporate governanceInternational tradeBusinessIndustrial organizationEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Standards are the cornerstone of digital trade; however, the ideal method of governance of digital trade networks using technical standards remains underexplored. This study establishes a theoretical framework by investigating the effect of technical standards on a digital trade network and exploring the policy implications of technical-standards-based hierarchical governance. The findings reveal the heterogeneous patterns of digital trade networks and their governance characteristics. Furthermore, international, regional, and national technical standards are integral to the hierarchical governance of digital trade networks. Digital trade flow diffuses through a digital trade network, wherein technical standards have different levels of compatibility and security, and digital-trading countries make trade-offs. The results highlight that digital-trading countries need to explicitly incorporate the compatibility and security of technical standards into trade agreements, promote the harmonization of digital standards, and advance international cooperation to guarantee the flow of digital trade and reap its benefits.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.281
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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